Automatic Estimation Process And Device For A Flight Parameter Vector In An Aircraft, As Well As Detection Methods And Assemblies For A Failure Affecting Such A Vector

ABSTRACT

Automatic estimation process and device for a flight parameter vector in an aircraft, as well as detection methods and assemblies for a failure affecting such a vector. 
     The detection device ( 1 ) comprises means ( 5 ) for estimating, on an observation window, a coefficient vector allowing to determine a linear relationship between the searched flight parameter vector and explanatory values, by implementing a PLS regression, and means ( 5 ) to use such a coefficient vector so as to calculate, thru a linear modeling, an estimated value of said flight parameter vector.

The present invention relates to an automatic estimation process and device for a flight vector parameter used by an aircraft system, in particular an electrical flying control system, as well as detection methods and assemblies for at least one failure affecting such a flight parameter vector.

It is known that an electrical flying control system of an aircraft, in particular an airplane, allows piloting and controlling the latter thru a flying control computer. Such a computer acquires a piloting set-point being given by the position of the control members in a manual piloting mode (with the help of a stick or a rudder bar) or by an automatic pilot in an automatic piloting mode, and it translates it into a piloting objective. Such piloting objective is then compared with the real state of the aircraft, being obtained thru measurements performed by sensors (anemometric, clinometric and inertial ones that supply the current values of the flight parameters (such as acceleration, incidence, etc.). The result is used to calculate, thru piloting laws, a position control set-point for mobile surfaces (rudders) of the aircraft. The application of a servo-control on an actuator linked to a rudder allows the latter to be adjusted in the desired position and thus to influence the aircraft trajectory.

In order to be in conformity with the navigability requirements in force, the usual solution consists in taking steps from redundant sensors into account. The implementation of such a solution implies the application of monitoring (namely to detect one or more failing information sources and to reject them) and passivation (namely to limit the effect and the propagation of a failure) principles. Calculating only one valid value and checking in parallel the whole sources constitute a so-called consolidation process.

The present invention has particularly as an object to estimate at least one parameter vector (as detailed below) relative to at least one flight parameter of an aircraft, in particular to improve the availability of anemometric, clinometric and inertial data.

It relates to an automatic estimation process, in real time, of a flight parameter vector used by an aircraft system, in particular an electrical flying control system.

To this end, according to the invention, said estimation process is remarkable in that the following sequence of successive steps is automatically implemented:

(a) the values being observed (namely measured or consolidated, as further explained below) are received from a plurality of explanatory values, an explanatory value representing an aircraft parameter being used in the following processings;

(b) on an observation window, a coefficient vector is estimated, allowing a linear relationship to be determined between the flight parameter vector being searched and said explanatory values, which relationship is relative to a linear modeling by implementing a PLS (for “Partial Least Squares”) regression as described in details hereinafter;

(c) such estimated coefficient vector minimizing the power of the model error on the observation window is used to calculate, thru said linear modeling, an estimated value of said flight parameter vector; and

(d) the so-estimated value is transmitted to user means (with the view in particular to a consolidation and/or a detection of failures).

Therefore, thanks to the invention, as explained hereinunder, it is possible to estimate, in real time, on a quick and precise way and at reduced cost, a flight parameter vector used in the control of the aircraft, and more particularly, in the development of the piloting laws.

The implementation of the present invention does not need for new sensors or specific gauges to be installed. It can be in particular performed in a flight computer and enables to have in real time with a sufficient precision for the related application, estimations of some flight parameters available.

It is to be noticed that:

-   -   the flight parameter vector being estimated according to the         present invention can be used in addition to the measured or         calculated values of such flight parameter; and     -   the estimation according to the invention, which supplies         information being available in real time, can in particular be         used by consolidation means to extend the availability of the         flight parameter(s) being considered.

In the framework of the present invention:

-   -   said observation window is relative to a plurality of successive         samples, the data being processed in a periodic way, each         successive value of a same flight parameter being considered         representing one sample;     -   a flight parameter vector comprises at least one value of said         flight parameter. Such flight parameter vector can comprise a         plurality of successive samples of such value (according to the         observation window). Such flight parameter vector can also         comprise a plurality of values of said flight parameter coming         from a plurality of different information sources (sensors,         consolidation means) of the aircraft. In such a case, if N         samples are considered, it is defined as a matrix. Such flight         parameter vector can also comprise a plurality of flight         parameters.

In a particular embodiment, the operations b) and c) are iteratively performed by using the PLS regression and observed values (i.e. measured or possibly consolidated) of said flight parameter vector.

The invention thus anticipates estimating at least one flight parameter y thru q different flight parameters (x₁, x₂, . . . , x_(q)), so-called explanatory variables. In other words, let us consider a system with at least one output (the flight parameter y to be estimated) and q inputs (x₁, x₂, . . . , x_(q)), said explanatory variables. The inputs and the outputs are observed on a finite horizon of N samples (the flying control computers being aimed at being digital). The observation window is denoted F_(n)=[n−N+1, . . . , n], n being the current sampling time. Said parameter y to be estimated can also be a consolidated parameter. Also, the explanatory variables x₁(i=1, . . . , q) collected into the matrix

$x = \begin{bmatrix} {x_{1}(1)} & \ldots & {x_{q}(1)} \\ \vdots & \ddots & \vdots \\ {x_{1}(N)} & \ldots & {x_{q}(N)} \end{bmatrix}$

can also be consolidated variables.

It is tried to establish a linear relationship between the flight parameter and the explanatory variables, i.e. it is tried to find a coefficient vector b[b₁ . . . b_(q)]^(T) such that

y=Xb+e

wherein e, a vector of N lines, represents the model error being also called reconstruction error and the superscript IT “′” indicates the transpose of the vector.

The estimation is implemented on the observation window F_(n) and leads to an estimated coefficient vector b(n)=[b₁(n) . . . b_(q)(n)]^(T) thereby minimizing the error power on the observation window and verifying:

y(n)=X(n)b(n)+e(n)

wherein e(n)=[e(n)e(n−1) . . . e(n−N+1)]^(T)

It is to be noticed that such modeling principle can apply, for a same flight parameter, to each of the sensors providing observations of such parameter.

Furthermore, advantageously, in a particular embodiment, a further input variable, so-called adjusting input is used so as to be able to consider non centred signals, and more generally any non modelled uncertainty, as further detailed below.

The present invention also relates to a first automatic detection method for an automatic detection of at least one failure affecting at least one flight parameter vector used by an aircraft system, in particular an electrical flying control system.

According to the invention, such first method is remarkable in that the following sequence of successive steps is performed on an automatic and repetitive way:

A/ by implementing the above mentioned process, on any observation window F_(n+1) there are determined:

-   -   a said a priori first estimation of said flight parameter, being         calculated with the help of explanatory variables being observed         on said observation window F_(n+1) and a coefficient vector         being estimated on the previous observation window F_(n); and     -   a said a posteriori second estimation of said flight parameter,         being calculated thru explanatory variables observed on said         observation window and a coefficient vector being also estimated         on such observation window F_(n+1);

B/ an observed value of said flight parameter vector is determined on said observation window F_(n+1); and

C/ a comparison is carried out between said first and second estimations and said observed value, making possible to detect at least one failure affecting such flight parameter vector.

Advantageously, at step C/, the following sequence of successive steps is carried out:

C1/ with the help of a decision function that is applied to said first estimation, to said second estimation and to the observed value of said flight parameter, a decision value is calculated;

C2/such decision value is compared to a threshold; and

C3/ a failure is detected when said decision value is higher than said threshold.

Moreover, advantageously:

-   -   said threshold is determined through detection probabilities and         false alarm; and     -   at step C3/, a failure is detected when said decision value is         higher than said threshold during a confirmation duration.

The present invention further relates to a second automatic detection method for detecting a malfunction of sensors in the aircraft.

According to the invention, such second method is remarkable in that:

A/ the development of the coefficients in the PLS regression, calculated by means of explanatory variables and the observed parameter is determined by implementing the above mentioned process; and

B/ the development of such coefficients so as to be able to detect a malfunction is analyzed upon a development change of such coefficients.

In a first variation:

-   -   at previous step A/, thru components of the coefficient vector,         a criterion is calculated, which is representative of the         intra-vectorial development of said coefficient vector; and     -   at step B/, such criterion is compared to a predetermined value         and a malfunction is detected when such criterion is higher than         said predetermined value during a confirmation duration.

Moreover, in a second variation:

-   -   at step A/, a criterion being representative of the statistics         of said coefficients is calculated with the help of the         coefficient vectors; and     -   at step B/, such criterion is compared to a predetermined value         and a malfunction is detected when such criterion is lower than         said predetermined value during a confirmation duration.

The present invention also relates to an automatic estimation device, in real time, of a flight parameter vector used by an aircraft system, in particular an electrical flying order system.

According to the invention, said detection device is remarkable in that it comprises:

-   -   means to receive the observed values from a plurality of         explanatory values;     -   means to estimate, on an observation window, a coefficient         vector allowing a linear relationship to be determined between         the flight parameter vector being searched and said explanatory         values, relating to a linear modeling, by implementing a PLS         regression;     -   means to use such estimated coefficient vector so as to         calculate, with the help of said linear modeling, an estimated         value of said flight parameter vector; and     -   means to transmit the so-estimated value to user means.

Such a device is advantageous, since it does not need the installation of new sensors or specific gauges. Moreover, it can be embedded in a flight computer and allows to supply, in real time, with a sufficient precision for the related application, estimations of some flight parameters.

The present invention further relates a first automatic detection assembly for a failure affecting at least one flight parameter vector used by an aircraft system, in particular an electrical flying order system. Such detection assembly comprises:

-   -   one device such as the one above mentioned, to determine:     -   a said a priori first estimation of said flight parameter; and     -   a said a posteriori second estimation of said flight parameter;     -   means to determine an observed value of said flight parameter         vector; and     -   means to perform a comparison between said first and second         estimations and said observed value, thereby allowing to detect         a failure affecting such flight parameter vector.

Furthermore, the invention comprises a second automatic detection assembly for the malfunction of a aircraft sensors, comprising:

-   -   one device such as the one above mentioned to determine the         development of the coefficients of the PLS regression being         calculated with the help of explanatory variables and the         observed parameter; and     -   means to analyze the development of such coefficient so as to be         able to detect a malfunction upon a development change for such         coefficients.

The present invention also relates to:

-   -   an aircraft system, in particular a electrical flying order         system including a device and/or an assembly such as the ones         above mentioned; and     -   an aircraft, in particular a transport airplane, being equipped         with one system, one device and/or an assembly such as above         mentioned.

The FIGS. of the accompanying drawings will make well understood how the invention can be implemented. On such FIGS., identical references denote similar elements.

FIG. 1 is the block diagram of a detection device according to the invention, as well as user means.

FIG. 2 schematically illustrates an electrical flying control system of an aircraft comprising a device according to the invention.

FIG. 3 schematically illustrates a linear regression algorithm being used for implementing the present invention.

FIG. 4 is a graph showing COR type plots for different decision functions. Such COR (for Operational Characteristic of the Receiver) plots represent the detection probability at a function of the false alarm probability.

FIG. 5 is a block diagram for failure detection means.

FIG. 6 schematically illustrated a detection algorithm being used by a particular embodiment alternative for a detection assembly according to the invention.

The device 1 according to the invention and schematically represented on FIG. 1 is intended to automatically estimate, in real time, a flight parameter vector being used by a system of an aircraft AC, in particular by an electrical flying control system 20.

According to the invention, said detection device 1 that is embedded on the aircraft AV, comprises:

-   -   means (link 3) to receive the observed values from a plurality         of explanatory values, from a usual assembly 4 of information         sources more detailed hereinunder. An explanation value         represents a flight parameter (acceleration, incidence, etc.) of         the aircraft AC, at least one value of which is used in the         processings implemented in the present invention;     -   means 5 for:     -   estimating, on an observation window F_(n) (more detailed         hereinunder, a coefficient vector allowing a linear relationship         to be determined between the flight parameter vector being         searched and said explanatory values, relating to a linear         modelling, and this by implementing a regression PLS (for         “Partial List Squares”); and     -   using such estimated coefficient vector to calculate, thru said         linear modelling, an estimated value of said flight parameter         vector; and     -   means (a link 6 being able to be divided in two links 6A, 6B,         6C) to transmit to so-estimated value to user means 7 and 8         further detailed hereinunder.

Thus, the device 1 according to the invention is able to estimate, in real time, in a quick and precise way and at reduced post, a flight parameter vector used in the control of the aircraft AC, and particularly in the development of the piloting laws.

Moreover, the device 1 also comprises measuring means (assembly 4) to measure on the aircraft AC the values being used to obtain said observed explanatory values. Such observed explanatory values can be values being directly measured or values being calculated from measurements or consolidated values.

The implementation of the present invention, being described in details hereinunder, does not need the installation of new sensors or specific gauges, the device 1 being able to use an assembly 4 of information sources, being already present on the aircraft AC. It can in particular be implemented in a flight computer and allows to provide, in real time, with a sufficient precision for the related applications, estimations of some flight parameters.

A flight parameter vector comprises at least one value of said flight parameter. Such flight parameter vector can comprise a plurality of successive samples of such value (according to the observation window). Such flight parameter vector can also comprise a plurality of values of said flight parameter coming from a plurality of information sources (sensors, consolidating means) of the aircraft. In such a case, if we consider N samples, it is defined under the form of a matrix. Such flight parameter vector can also comprise a plurality of flight parameters.

In a preferred embodiment, the means 5 perform iterative processings, more detailed hereinunder, by using the PLS regression and observed values (i.e. measured or possibly consolidated) of said flight parameter vector. To this end, the device 1 also comprises measuring or consolidating means 10 being connected by a link 11 to the means 5 so as to generate and provide the observed values of said flight parameter vector, that will be used by said means 5.

It is to be noticed that the estimated flight parameter vector according to the invention can specifically be used:

-   -   in addition of said measured or calculated values of such flight         parameter, while being transmitted to means (computers, etc.),         not shown, thru the link 6 a;     -   by failure detection means 7, more details hereinunder, that         form with the device 1 a failure detection assembly 12 which is         adapted to indicate a failure via a link 13; and/or     -   by consolidation means 8 to extend the availability of the         flight parameter(s) being considered.

Usually, the consolation means 8 are part of a consolidation assembly 15 comprising moreover information sources 16. Such information sources 16 are connected by a link 17 to the means 8 and provide redundant values of the flight parameter being considered.

The consolidation means 8 use the value being estimated by the device 1 and the values being received from the information sources 16 so as to determine, on a usual way, a consolidated value of the flight parameter which can be transmitted by a link 18.

Furthermore, in a preferred embodiment, the device 1 is part of an electrical flying control system 20 of an aircraft AC, in particular a flying transport airplane, such as represented on FIG. 2. Although, for clarity and comprehensive facility reasons, the system 20 is represented besides the aircraft AC on FIG. 2 it is of course embedded on the latter.

On a usual way, such electrical flying order system 20 comprises:

-   -   at least one rudder bar (aileron, spoiler, depth or direction         rudder) that is mobile and the position of which, with respect         to the aircraft AC, is adjusted by at least one actuator 21;     -   said actuator 21 adjusting the position of the rudder as a         function of at least one actuating order being received;     -   a sensor assembly 22; and     -   a flying control computer 23 that develops an actuating order         for the rudder, which is transmitted to the actuator 21, from a         control order calculated for example from the action of the         pilot on control stick 24 or a piloting set-point received from         the automatic pilot PA.

Such flight control computer 23 can generally comprise:

-   -   consolidation means 26;     -   piloting objective calculation means 27;     -   comparison means 28;     -   order calculation means 29; and     -   a servo loop 30.

The computer 23 acquires a piloting set-point which is given by the position of the control members in a manual piloting mode (with the help of a stick 24 and a rudder bar) or by the automatic pilot PA in an automatic piloting mode, and it translates it into a piloting objective (means 27).

Such piloting objective is then compared (means 28) to the real condition of the aircraft AC, which is obtained thru measurements performed by sensors 22 (anemometric, clinometric and inertial ones) that provide the current values of flight parameters (such as acceleration, incidence, etc.). The result is used to calculate, thru piloting laws (means 29), a position servo set-point for the rudders of the aircraft. The application of a servo control (means 30) on an actuator 21 connected to a rudder allows the latter to be adjusted in the desired position and thus to influence the trajectory of the aircraft AC.

The device 1 can be integrated into the system 20, in particular on the way represented on FIG. 2. In such a case, the estimation means 5 of the device 1 directly use the values received from the sensors 22 (or possibly from the consolidation means 26) and transmit the performed estimation to the consolidation means 26 (or possibly to the comparison means 28).

The estimation implemented by the means 5 according to the present invention will be described below.

The invention thus envisages at least one flight parameter y to be estimated thru q different flight parameters (x₁, x₂, . . . , x_(q), so-called explanatory variables. It is also possible to formalize the problem on the following way: let us consider a system with one output (the parameter y to be estimated) and q inputs (x₁, x₂, . . . , x_(q)), namely the explanatory variables. The inputs and the outputs are observed on a finite horizon of N samples (the computers being consider being of a digital type).

The observation window is denoted F_(n)=[n−N+1, . . . , n], n being the current sampling time.

It is denoted:

-   -   y(n)=[y(n)y(n−1) . . . y(n−N+1)]^(T), the vector of N lines of         the samples of the parameter y being measured by one of the         sensors on the observation window, the exponent “′” indicating         the transpose of the vector; and     -   X(n)=└x₁(n)x₂(n) . . . x_(q)(n)┘ the input matrix of N lines and         q columns of the samples of the q explanatory variables measured         on the observation window, with x₁(n)=[x_(i)(n)x_(i)(n−1) . . .         x_(i)(n−N+1)]^(T) and i=1, . . . , q.

It is tried to establish a linear relationship between the parameter and the explanatory variables, i.e. it is tried to find the coefficient vector b=[b₁ . . . b_(q)]^(T) such that:

y=Xb+e

wherein e, a vector of N lines, represents the model error also called the reconstruction error.

The estimation is performed on the observation window F_(n) and lead to an estimated coefficient vector b(n)=[b₁(n) . . . b_(q)(n)]^(T) minimizing the error power on the observation window and meeting the following equation Eq1:

y(n)=X(n)b(n)+e(n)

wherein e(n)=[e(n)e(n−1) . . . e(n−N+1)]^(T)

It is important to note that such a modeling principle can apply, for a same flight parameter, to each of the sensors supplying observations of such parameter (y_(i) (i=1, . . . s) or to a consolidated value y_(c)).

Moreover, such linear modeling principle entre the q inputs being observed on a window of N samples and the output y representing a flight parameter to be estimated can also be easily generalized to the case of a flight parameter vector. In such a case, a vector y is no longer considered, but a matrix Y made of the r flight parameters to be estimated on a horizon of N samples:

Y(n)=[y₁(n) y₂(n) . . . y_(r)(n)] being the matrix of parameters with N lines and r columns, with y_(i)(n)=[y_(i)(n)y_(i)(n−1) . . . y_(i)(n−N+1)]^(T) and i=1, . . . , r.

Also, in the equation Eq1, the coefficients of the linear model are grouped together, no longer in a vector, but in a matrix of q lines and r columns. However, with no loss of generality, hereinunder, the case of an only one output will be detailed to simplify the explanations. The method is also easily generalized to the case of r outputs.

The solution is obtained via a least squares method being adapted to minimize the error power between the actual output y and the estimated output ŷ=Xb.

The PLS regression (for “Partial Least Squares”) is used, which is an alternative to the usual least squares method, and which is not very expensive in resources (calculation power and memory at the level of the computers).

In the absence of anomalies, the model follows the development of the flight parameter y. This allows the short term prediction of such parameter to be envisaged from the knowledge of the estimated model. The coefficients b(n) calculated by the PLS regression on the window F_(n), thru the explanatory variables X(n) et the parameter y(n) being observed, will enable to predict the parameter on the window F_(n+1) according to:

{tilde over (y)}(n+1)=X(n+1)b(n).

Such prediction {tilde over (y)}(n+1) is an a priori estimation of y(n+1) calculated from b(n). Such a priori estimation can be compared to an a posteriori estimation calculated from b(n+1) being defined by:

ŷ(n+1)=X(n+1)b(n+1)

wherein b(n+1) represents the vector of the coefficients calculated by the regression PLS on the window F_(n+1) thru the explanatory variables X(n+1) and the parameter y(n+1) being observed. There are thus obtained two estimations of y(n+1).

These a priori and a posteriori estimations can relate to y(n+1), as detailed in the above mentioned expressions, but can also be easily extended to a larger horizon to estimate y(n+i).

The failure detection (implemented by the means 7) is based on the comparison of both such estimations {tilde over (y)}(n+1) and ŷ(n+1) of the observed parameter y(n+1) (received from the means 10 for example) and is explained hereinunder.

First, the PLS regression principle will be presented.

To simplify the explanations, the annotations being used in the following omit the indication of the current time n: thus, X(n),y(n),b(n) will be denoted X, y and b.

La PLS regression allows the following system to be solved:

y=Xb+e

with no explicit matricial inversion. To this end, y and the columns of X are projected into a same dimension space a≦q, the number of explanatory variables. Thus, we are looking for the matrix T (N lines, a columns, a≦q), the matrix V (q lines, a columns) and the vector c (a lines) such that:

$\quad\begin{Bmatrix} {X = {{TV}^{\prime} + R_{x}}} \\ {y = {{Tc} + r_{y}}} \end{Bmatrix}$

and such that the residue matrix R_(x) and the residue vector r_(y) are minimum.

The PLS regression is an iterative algorithm enabling to do so.

The first step of the PLS regression consists in calculating t₁ representing the first column of T according to:

$t_{1} = \frac{{Xw}_{1}}{w_{1}^{\prime}w_{1}}$

with w₁ being a vector of q lines:

$w_{1} = \frac{X^{\prime}y}{y^{\prime}y}$

Afterwards, the regression of X and y on such first component t₁ is carried out:

$\quad\begin{Bmatrix} {X = {{t_{1}v_{1}^{\prime}} + R_{x\; 1}}} \\ {y = {{t_{1}c_{1}} + r_{y\; 1}}} \end{Bmatrix}$

with v₁ being the first column of V and c₁ the first component of the vector c. The calculation of v_(k) and C_(k) is explained in the algorigram of FIG. 3, whatever k. If the reconstruction error id judged as too large, a new component t₂ is calculated from the residues R_(x1) and r_(y1) two new models are established, with two components:

$\quad\begin{Bmatrix} {X = {{t_{1}v_{1}^{\prime}} + {t_{2}v_{2}^{\prime}} + R_{x\; 2}}} \\ {y = {{t_{1}c_{1}} + {t_{2}c_{2}} + r_{y\; 2}}} \end{Bmatrix}$

in which expressions t₂ and v₂ represent respectively the second columns of T and V and c₂ the second component of the vector c.

The procedure is repeated by inserting new components and by establishing a new model of k components until the reconstruction error is acceptable by the user. It will be noticed a≦q as the final number of components. Thus, the a posteriori estimation ŷ is expressed on a simple way (according to the following equation Eq2):

$\hat{y} = {\sum\limits_{k = 1}^{a}{c_{k}t_{k}}}$

Moreover, the coefficients b of the linear model can also be obtained on an iterative way. At each iteration of an index k, the component t_(k) is defined from X_(k-1) by the relationship:

$t_{k} = \frac{X_{k - 1}w_{k}}{w_{k}^{\prime}w_{k}}$

with:

X ₀ =X

X _(k) =X _(k-1) −t _(k) v′ _(k)

The components t_(k) can also be expressed as a function of X (according to the following equation Eq3):

t _(k) =Xw _(k)*

wherein the vectors w_(k)* are linked to the vectors w_(k) thru the relationship:

w ₁ =w ₁

$w_{k}^{*} = {\prod\limits_{j = 1}^{k - 1}\; {\left( {I - {w_{j}v_{j}^{\prime}}} \right)w_{k}}}$

for k>1

The w_(k)* can be calculated by recurrence:

w ₁ *=w ₁

w _(k) *=w _(k) −w _(k) *v′ _(k-1) w _(k)

The equations Eq2 and Eq3 can be written:

$\hat{y} = {{\sum\limits_{k = 1}^{a}{c_{k}{Xw}_{k}^{*}}} = {X{\sum\limits_{k = 1}^{a}{c_{k}w_{k}^{*}}}}}$

This allows giving the expression of the coefficients b:

$b = {\sum\limits_{k = 1}^{a}{c_{k}w_{k}^{*}}}$

The PLS regression principle above described is an iterative algorithm as explained in the algorigram of FIG. 3, wherein the following annotations are used:

k Index of the pending iteration; X_(k) Input matrix, N lines, q columns on iteration k; y_(k) Output vector, N lines on iteration k; b_(k) Vector of the coefficients such as y_(k)=X_(k)b_(k) on iteration k; a Number of PLS components being retained, lower than or equal to q; w_(k) Vector of q lines, regression coefficients of y_(k-1) in the regression of the j-th column of X_(k-1) on y_(k-1); w_(k)* Vector of q lines intervening in the update of b; t_(k) Vector of N lines, regression coefficients of w_(k) in the regression of the i-th line of X_(k-1) on w_(k); V_(k) Vector of q lines, regression coefficients of t_(k) in the regression of the j-th column of X_(k-1) on t_(k); c_(k) Regression coefficient of y_(k-1) on t_(k); and ŷ Estimation of the PLS regression.

Moreover, on FIG. 3:

O1 corresponds to “yes” and N1 to “no”;

A0 illustrates the set of data being entered;

D indicates the start of the algorithm of the PLS regression;

AF indicates the end of such algorithm; and

in L1, w_(k) is normalized to 1.

Furthermore, as regards the failure detection strategy (implemented by the means 7), it is based, at a given time n+1, on the comparison of the two a priori {tilde over (y)}(n+1) and a posteriori ŷ(n+1) estimations and of the observed value y(n+1).

Both following hypotheses are defined:

-   -   H₀: absence of a malfunction; and     -   H₁: presence of a malfunction.

The decision between these two hypotheses uses a function F of both estimations and of the observed value, according to the following rule:

${F\left( {{\overset{\sim}{y}\left( {n + 1} \right)},{\hat{y}\left( {n + 1} \right)},{y\left( {n + 1} \right)}} \right)}\overset{H_{1}}{\underset{H_{0}}{> <}\lambda}$

with λ being a threshold value to be defined. If the function F, so-called test statistic or decision function, is higher than the threshold, then the hypothesis H₁ is verified, i.e. a malfunction (or failure) is detected (thru the means 7), as represented on FIG. 5 (with O2 for yes).

If the function F is lower than the threshold, then the hypothesis H₀ is verified, and the means 7 conclude to an absence of malfunction (or failure), as represented on FIG. 5 (with N2 for no).

The selection of the threshold A is carried out as a function of probabilities about detection (P_(D)) and false alarm (P_(FA))

Generally, it is convenient to fix one of the two probabilities, for example P_(FA), thereby enabling to calculate a threshold and to infer therefrom the other probability P_(D). It is then possible to plot a curve COR (for Operational Characteristic of reception) representing the detection probability P_(D) as a function of the false alarm probability P_(FA) for different threshold values. Such a curve allows the detection strategy to be characterized and different possible functions F to be compared.

Thus, as illustrated on FIG. 4, the function F₁ leads to better performances than F₂. The function F₃ corresponds to the less performing solution, whereas the thick line curve corresponds to the ideal solution being looked for.

The overall principle of such malfunction detector (means 7) is represented on FIG. 5.

For illustration (and not limitative) sake, several expressions of the function F to be applied can be proposed.

From a priori and a posteriori estimations as previously defined, the corresponding estimation errors are calculated:

{tilde over (e)}(n+1)=y(n+1)−{tilde over (y)}(n+1)

ê(n+1)=y(n+1)−ŷ(n+1)

The power {tilde over (P)} of the a priori {tilde over (e)} error is higher than the power {circumflex over (P)} of the a posteriori ê error, since the latter is calculated so as to be minimum.

A first decision function, that could be proposed, only takes into account the a priori {tilde over (y)} estimation and the observed value y:

F ₁({tilde over (y)},y)={tilde over (P)}

However, it can be interesting to compare such a priori estimation and the a posteriori estimation. Two other functions can thus be proposed:

F ₂({tilde over (y)},ŷ,y)={circumflex over (P)}−{tilde over (P)}

F ₃({tilde over (y)},ŷ,y)={hacek over (P)}

{hacek over (P)} representing the power of the difference between the a priori and a posteriori estimations:

{hacek over (e)}(n+1)=ŷ(n+1)−{tilde over (y)}(n+1).

Furthermore, it will be noticed that the input parameters can take punctually aberrant values (for example due to a transmission problem at the level of the data bus), thereby leading thru a PLS regression to incoherent a priori and/or a posteriori estimations. The above mentioned detection strategy is based on a thresholding of the instantaneous value of the error power and can thus lead in such precise case to false alarms.

The establishment of a confirmation strategy allows the detection to be sturdier.

To do so, the decision function is extended to the times └n+1), (n+2), . . . , (n+T_(conf))┘ constituting the confirmation window. Thus, if the decision function goes beyond the fixed threshold during a (predetermined) percentage of the (also predetermined) confirmation time T_(conf), the malfunction is confirmed.

Furthermore, within the present invention, the PLS regression can be extended to non centered signals.

It is known that, in the case of usual least squares, it is tried to solve the following system:

ŷ=Xb.

If X is centered, ŷ is the same. Now, ŷ represents an estimation of y that should thus also be centered. It is a necessary condition to select a linear relationship.

Thus, if X and y are centered before carrying out the resolution of the least squares, or possibly thru the PLS regression, the following equation Eq4 is obtained:

ŷ−m _(y)·1_({N*1})=(X−M _(x))b

avec 1_({N*1}) being a unit vector of N lines and M_(x) a matrix of N lines, q columns such that:

$M_{x} = \begin{pmatrix} m_{x\; 1} & \ldots & m_{xq} \\ \vdots & \ddots & \vdots \\ m_{x\; 1} & \ldots & m_{xq} \end{pmatrix}$

m_(y) and m_(xi) correspond to the averages of y and the i-th column of X.

The equation Eq4 becomes the following equation Eq5:

$\hat{y} = {\begin{pmatrix} {{x_{1}(n)} - m_{x\; 1}} & \ldots & {{x_{q}(n)} - m_{xq}} & 1 \\ \vdots & \ddots & \vdots & \vdots \\ {{x_{1}\left( {n - N + 1} \right)} - m_{x\; 1}} & \ldots & {{x_{q}\left( {n - N + 1} \right)} - m_{x_{q}}} & 1 \end{pmatrix}\begin{pmatrix} b_{1} \\ \vdots \\ b_{q} \\ m_{y} \end{pmatrix}}$

The centring of the outputs and the input is necessary for a good resolution of the system. However, in the present case:

centring y would eliminate some failures, including the bias type failures occurring thru an average jump; and

centring X could lead to false alarms. Indeed, a (natural) average jump on the input variables can lead to an average jump on the output, which would be considered as a normal operation. Centring the inputs would lead to consider the output jump as a malfunction, and would thus cause a false alarm.

In order to remedy such problem, advantageously, a so-called adjusting input variable is added. Indeed, the equation Eq5 can also be written:

$\hat{y} = {\begin{pmatrix} {x_{1}(n)} & \ldots & {x_{q}(n)} & 1 \\ \vdots & \ddots & \vdots & \vdots \\ {x_{1}\left( {n - N + 1} \right)} & \ldots & {x_{q}\left( {n - N + 1} \right)} & 1 \end{pmatrix}\begin{pmatrix} b_{1} \\ \vdots \\ b_{q} \\ {m_{y} - {\sum\limits_{k = 1}^{q}{b_{k}m_{x_{k}}}}} \end{pmatrix}}$

A linear model between y and X is looked for without previously centring them, but by inserting an extra input variable. The model being looked for is written:

$\hat{y} = {\begin{pmatrix} {x_{1}(n)} & \ldots & {x_{q}(n)} & 1 \\ \vdots & \ddots & \vdots & \vdots \\ {x_{1}\left( {n - N + 1} \right)} & \ldots & {x_{q}\left( {n - N + 1} \right)} & 1 \end{pmatrix}\begin{pmatrix} {b_{1}^{+}(n)} \\ \vdots \\ {b_{q}^{+}(n)} \\ {b_{q + 1}^{+}(n)} \end{pmatrix}}$

with b⁺(n)=[b₁ ⁺(n) . . . b_(q+1) ⁺(n)] being a vector of q+1 lines. It constitutes an increased version of the vector b by adding a component. Such component represents:

$m_{y} - {\sum\limits_{k = 1}^{q}{b_{k}m_{x_{k}}}}$

with the hypothesis of a linear relationship between X and y. In the other cases, such component allows the average differences between y and Xb to be compensated.

The addition of the adjusting variable enables to look for the function minimizing the error in the set of the affine functions rather than in the one of the linear functions. The set, in which the function is looked for, is larger and thus allows a function to be found, that will give a lower or an equal reconstruction error.

Indeed, both approaches can be compared, with and without adjusting variable:

A/ without the adjusting variable, g₁ is looked for such that:

y=g ₁(X)+e ₁

avec g₁ being:

^(N)×

^(q)→

^(N) ,bε

^(q) X→Xb

Let us denote G₁ the set of the functions g₁; and

B/ with the adjusting variable, g₂ is looked for such that:

y=g ₂(X)+e ₂

with g₂ being:

^(N)×

^(q)→

^(N) ,bε

^(q) etb _(q+1) ⁺ ε

X→Xb+b _(q+1) ⁺·1_({N*1})

Let us denote G₂ the set of the functions g₂.

The reconstruction error e is to be minimized, i.e. it is looked for

ĝ ₁ εG ₁ and ĝ ₂ εG ₂ such that:

ĝ ₁=arg_(g) ₁ min|y−g ₁(X)|²=arg_(g) ₁ min|e ₁|²

and

ĝ ₂=arg_(g) ₂ min|y−g ₂(X)²|=arg_(g) ₂ min|e ₂|²

The functions ĝ₁ and ĝ₂ possess q input variables and one output variable.

If the minimum error is obtained via g₁ then g₂ will fix the coefficient b_(q+1) ⁺ to 0. In such a case, G₁ is well included in G₂.

Otherwise, g₂ will permit the minimization of the error by proposing an affine solution rather than linear, and, in such a case, G₁ is still included in G₂.

Thus, G₁ is included in G₂, which infers:

arg_(g) ₂ min|e ₂|²≦arg_(g) ₁ min|e ₁|²

Consequently, ĝ₂ leads either to the same error e than ĝ₁, or to a weaker error.

Finally, the addition of the adjusting variable thus leads necessarily to a weaker (or at most equal) reconstruction error.

The device 1 according to the invention can also be applied to another detection assembly (not represented), allowing the malfunction of the sensors of the aircraft to be automatically detected.

Such automatic detection assembly comprises, in addition to said device 1 (being used for determining the development of the coefficients of the PLS regression, calculated thru explanatory variables and the observed parameter), means (not represented) to analyze the development of such coefficients so as to be able to detect a malfunction upon a development change of such coefficients.

It is known that the coefficients b⁺=[b₁ ⁺ . . . b_(q) ^(+b) _(q+1) ⁺], being coefficients associated with the q inputs (x₁, x₂, . . . , x_(q)) and with the adjusting variable, present a behavior change when a malfunction occurs. It is possible to use such change so as to detect the malfunction.

Two approaches are provided: one (first) analysis of the intra-vectorial development of b⁺=[b₁ ⁺ . . . b_(q) ^(+b) _(q+1) ⁺] and a (second) analysis of the statistical development of the coefficients b⁺ over the time. In the following, b⁺(n)=[b₁ ⁺(n) . . . b_(q+1) ⁺(n)] denotes le vector b⁺ being calculated at the instant n.

The detection strategy (relative to the analysis of the intra-vectorial development) consists in doing the follow-up of the dispersion for the coefficients coming from the PLS regression. This amounts to evaluating on each sample a distance between the components of the coefficient vector b⁺. A particular analysis deriving from such strategy is detailed hereinunder.

The dispersion of the vector is measured by the power thereof, that allows abrupt changes of average and variance (Eq6) to be taken into account simultaneously:

${C_{1}(n)} = {{\frac{1}{q + 1}{\sum\limits_{i = 1}^{q + 1}{b_{1}^{+ 2}(n)}}} = {\frac{{b^{+}}^{2}}{q + 1}.}}$

Other criteria are also to be envisaged, such as the criteria of the average

$\frac{1}{q + 1}{\sum\limits_{i = 1}^{q + 1}{b_{i}^{+}(n)}}$

or the variance

${\frac{1}{q + 1}{\sum\limits_{i = 1}^{q + 1}\left( {{b_{i}^{+}(n)} - {\frac{1}{q + 1}{\sum\limits_{j = 1}^{q + 1}{b_{j}^{+}(n)}}}} \right)^{2}}},$

but they only take into account a part of the information carried by the coefficients. Thus, for the sake of clarity, only the results associated with the criterion Eq6 are presented.

The test rule is as follows:

$\begin{matrix} {\mspace{14mu} H_{1}} \\ {{{C_{1}(n)}\begin{matrix}  > \\  <  \end{matrix}{seuil}},} \\ {\mspace{14mu} H_{0}} \end{matrix}$

H₀ represents the hypothesis of a normal operation and H₁ the hypothesis of a presence of failure. We look to see if said criterion is higher than at least one threshold, and this during at least a certain confirmation time.

Furthermore, the second detection strategy consists in analyzing a change in the statistics of the coefficients b⁺. Indeed, it can be supposed that, in the absence of a failure (hypothesis H₀), the vector b⁺=[b₁ ⁺ . . . b_(q) ⁺b_(q+1) ⁺] J follows a law p_(H) ₀ (b⁺) the parameters of which can be estimated. On the contrary, in presence of a failure (hypothesis H₁), the vector follows a different law p_(H) ₁ (b⁺) depending on the type of failure and the characteristics thereof.

Since a detection strategy is wished, that is adapted for any type of failure, it is of no question to characterize the different laws being possible under H₁, all the more because it is not sure that the set of the failures to be detected is entirely defined. Thus, the detection problem amount to testing the following hypotheses:

H ₀ :b ⁺ follows p_(H) ₀ H ₁ :b ⁺ does not follow p_(H) ₀

Supposing that the laws under H₀ and H₁ are sufficiently distant, a test law may be the following (Eq7):

$\begin{matrix} {\mspace{40mu} H_{0}} \\ {{P_{H_{0}}\left( b^{+} \right)}\begin{matrix}  > \\  <  \end{matrix}{seuil}_{1}} \\ {\mspace{34mu} H_{1}} \end{matrix}$

or on an equivalent way by using the log-likelihood (Eq8):

$\begin{matrix} {\mspace{115mu} H_{0}} \\ {\sum\limits_{i = 1}^{q + 1}{{\ln \left( {p_{H_{0}}^{(i)}\left( b^{+} \right)} \right)}\begin{matrix}  > \\  <  \end{matrix}{seuil}_{2}}} \\ {\mspace{110mu} H_{1}} \end{matrix}$

Any other function C₂(p_(H) ₀ (b⁺)) from p_(H) ₀ (b⁺) can be used.

We look if the criterion is lower or higher than at least one threshold during at least a certain confirmation time. The threshold should be selected depending on the desired false alarm and non detection probabilities.

Insofar as the hypothesis H₁ is not entirely specified (p_(H) ₁ not known), only the false alarm probability can be fixed, thereby determining the test threshold:

P _(FA)=∫∫_(D) ₁ p _(H) ₀ (b ⁺)

wherein D₁ corresponds to the part of

^(q+1) meeting H₁ in Eq7, i.e. the set of the components of b⁺ such that p_(H) ₀ (b⁺)<seuil₁.

Thus, the calculation of the P_(FA) et the threshold occurs on the distribution queue p_(H) ₀ .

In order to determine the type of law followed by the vector b⁺ under H₀, it is supposed that the different components are independent:

p _(H) ₀ (b ⁺)=Π_(f=1) ^(q+1) p _(H) ₀ ^((i))(b _(i) ⁺),

with p_(H) ₀ ^((i)) being the margin laws of the different components.

The different margin laws are selected amongst the known laws (or an assembly of known laws) so as to be the closest possible to the histogram of the coefficients originating from the PLS regression.

In general, the known laws that can approximate the b⁺ statistic are function of two parameters, average and variance. The principle stays however the same with more complex and more general laws with three parameters, and the detection algorithm should be adapted consequently.

The detection algorithm is presented by the algorigram (schematic representation of the process being implemented in real time) of FIG. 6 in five steps (E1 to E5) disclosed hereinunder.

On such FIG. 6:

01 corresponds to “yes” and N1 to “no”;

D denotes the start of the detection algorithm;

AF denotes the end of such algorithm;

DET corresponds to the detection;

conv corresponds to the convergence time of the calculated average;

fin_(ech) to the last point of the sample;

crit corresponds to the criterion of the strategy used for the detection; and

threshold corresponds to the detection threshold.

In the first time (step E1), the parameters for the calculation of the detection criterion, namely the average and the standard deviation of each component of the vector b⁺(n) of the PLS, are initialized.

It is taken account of the following elements:

n=0

m _(n) ^((i))=0

m2_(n) ^((i))=1, i=1, . . . , q+1.

σ_(n) ^((i))=√{square root over (m2_(n) ^((i)) −m _(n) ^((i)) ² )}{square root over (m2_(n) ^((i)) −m _(n) ^((i)) ² )}

Thus, m_(n)=0 where m_(n) is a vector of q+1 lines of the averages of each component of the vector b⁺ at the time n:m_(n)=(m_(n) ⁽¹⁾ . . . m_(n) ^((q+1)))^(T).

m2_(n)=(1 . . . 1)^(T) is a vector of q+1 intermediate lines allowing for the calculation of the standard deviation.

σ_(n)=(1 . . . 1)^(T) is a vector of q₊1 lines of the standard deviations for each component of the vector b⁺ at the time k:σ_(n)=(√{square root over (m2_(n) ⁽¹⁾−(m_(n) ⁽¹⁾)²)}{square root over (m2_(n) ⁽¹⁾−(m_(n) ⁽¹⁾)²)} . . . √{square root over (m2_(n) ^((q))−(m_(n) ^((q)))²)}{square root over (m2_(n) ^((q))−(m_(n) ^((q)))²)})^(T)

At step E2, the time n is considered.

At step E3, the vector b⁺(n) of q+1 lines of the coefficients from the PLS regression at the time n is calculated.

At step E4, the new values of the parameters of the probability law are updated. In the example being considered, it is a weighted average. Generally, such updating is performed at each time on the following way (Eq9):

m _(n) =m _(n−1)λ₁ +b _(n) ⁺(1−λ₁)

with 0≦λ₁≦1.

However, at the very start, after initialization, such updating cannot be considered as valuable and usable for the detection as long as the value has not sufficiently converged. In practice, it is considered that the average value calculated by Eq9 being valuable when the calculated average has reached at least 90% of the desired value. In order to be able to calculate the necessary convergence time, the hypothesis is made that during the convergence time the b⁺(n) are constant. The equation Eq9 becomes:

$\begin{matrix} {m_{n} = {{m_{0}\lambda_{1}^{n}} + {\left( {1 - \lambda_{1}} \right){\sum\limits_{j = 0}^{n - 1}{{b^{+}\left( {n - j} \right)}\lambda_{1}^{j}}}}}} \\ {\approx {{m_{0}\lambda_{1}^{n}} + {{b^{+}(n)}{\sum\limits_{j = 0}^{n - 1}{\left( {1 - \lambda} \right){\lambda_{1}^{j}.}}}}}} \end{matrix}$

In the present case, m₀=0. Thus, finding the convergence time amounts to determining the minimum value of n for which Σ_(j=0) ^(n−1)(1−λ)λ₁ ^(j) goes beyond 90%. An initialization is performed before starting the malfunction detection and going thru the step E5.

At step E5, following elements are taken into account:

m _(n) ^((t)) =m _(n−1) ^((i)) 33 λ₁ +b ^(+(i))(n)(1−λ₁)

m2_(n) ^((i)) =m2_(n−1) ^((i))×λ₁ +b ^(+(i)) ² (n)(1−λ₁), i=1, . . . , q+1.

σ_(n) ^((i))=√{square root over (m2_(n) ^((i)) −m _(n) ^((i)) ² )}{square root over (m2_(n) ^((i)) −m _(n) ^((i)) ² )}

At step E5, the detection criterion is calculated, for example the one defined by Eq8.

In order to avoid the false alarms related to aberrant values, the average of such criterion is done on a certain number of confirmation points n_(conf). Further to switching to the logarithm, such average is approximated by the maximum over the confirmation time.

The rule mentioned in Eq7 enables to decide either there is or not a detection (highlighted by DET on FIG. 6). 

1. An automatic estimation process of a flight parameter vector used by a system (20) of an aircraft (AC), in particular an electrical flying control system, wherein the following sequence of successive steps is automatically implemented: (a) the values being observed are received from a plurality of explanatory values, an explanatory value representing a parameter of the aircraft (AC) being used in the following processings; (b) on an observation window, a coefficient vector is estimated, allowing a linear relationship to be determined between the flight parameter vector being searched and said explanatory values, which relationship is relative to a linear modeling by implementing a PLS regression; (c) such estimated coefficient vector minimizing the power of the model error on the observation window is used to calculate, thru said linear modeling, an estimated value of said flight parameter vector; and (d) the so-estimated value is transmitted to user means.
 2. The process according to claim 1, wherein a flight parameter vector comprises at least one value of said flight parameter and at least one flight parameter.
 3. The process according to claim 1, wherein said observation window is defined for a plurality of successive samples.
 4. The process according to claim 1, wherein the operations b) and c) are iteratively performed by using the PLS regression and observed values of said flight parameter vector.
 5. The process according to claim 1, wherein a further input variable, so-called adjusting input is used so as to be able to consider non centred signals, and more generally any non modelled uncertainty
 6. An automatic detection method for one failure affecting at least one flight parameter vector used by an aircraft system, in particular an electrical flying control system, wherein the following sequence of successive steps is performed on an automatic and repetitive way: A/ by implementing the process specified in claim 1, on any observation window F_(n+1) are determined: a said a priori first estimation of said flight parameter, being calculated with the help of explanatory variables being observed on said observation window F_(n+1) and a coefficient vector being estimated on the previous observation window F_(n); and a said a posteriori second estimation of said flight parameter, being calculated thru explanatory variables observed on said observation window F_(n+1) and a coefficient vector being also estimated on such observation window F_(n+1); B/ an observed value of said flight parameter vector is determined on said observation window F_(n+1); and C/ a comparison is carried out between said first and second estimations and said observed value, making possible to detect at least one failure affecting such flight parameter vector.
 7. The method according to claim 6, wherein at step C/, the following sequence of successive steps is carried out: C1/ with the help of a decision function that is applied to said first estimation, to said second estimation and to the observed value of said flight parameter, a decision value is calculated; C2/ such decision value is compared to a threshold; and C3/ a failure is detected when said decision value is higher than said threshold.
 8. The method according to claim 6, wherein said threshold is determined with the help of detection and false alarm probabilities.
 9. The method according to claim 6, wherein at step C3/, a failure is detected when said decision value is higher than said threshold during a confirmation time.
 10. The automatic detection method for a malfunction of sensors in an aircraft, wherein: A/ the development of the coefficients in the PLS regression, calculated by means of explanatory variables and the observed parameter is determined by implementing the process specified in claim 1; and B/ the development of such coefficients so as to be able to detect a malfunction is analyzed upon a development change of such coefficients.
 11. The method according to claim 10, wherein: at step A/, thru components of the coefficient vector, a criterion is calculated, which is representative of the intra-vectorial development of said coefficient vector; and at step B/, such criterion is compared to a predetermined value and a malfunction is detected when such criterion is higher than said predetermined value during a confirmation duration.
 12. The method according to claim 10, wherein: at step A/, a criterion being representative of the statistics of said coefficients is calculated with the help of the coefficient vectors; and at step B/, such criterion is compared to a predetermined value and a malfunction is detected when such criterion is lower than said predetermined value during a confirmation duration.
 13. An automatic estimation device for a flight parameter vector used by a system (20) of an aircraft (AC), in particular an electrical flying order system, wherein it comprises: means (3) to receive the observed values from a plurality of explanatory values, an explanatory value representing one parameter of the aircraft (AC) being used in the following processings; means (5) to estimate, on an observation window, a coefficient vector allowing a linear relationship to be determined between the flight parameter vector being searched and said explanatory values, relating to a linear modeling, by implementing a PLS regression; means (5) to use such estimated coefficient vector minimizing the power of the model error on the observation window so as to calculate, with the help of said linear modeling, an estimated value of said flight parameter vector; and means (6A, 6B, 6C) to transmit the so-estimated value to user means (7, 8).
 14. The device according to claim 13, wherein it comprises, in addition, measurement means (4, 10) so as to measure on the aircraft (AC) values being used to obtain said observed values.
 15. An automatic detection assembly for a failure affecting at least one flight parameter vector used by a system (4, 10) of an aircraft (AC), in particular an electrical flying control system, wherein it comprises: a device (1) such as the one specified in claim 10, to determine on any observation window F_(n+1): a said a priori first estimation of said flight parameter being calculated with the help of explanatory variables observed on said observation window F_(n+1) and an estimated coefficient vector on the preceding observation window F_(n); and a said a posteriori second estimation of said flight parameter being calculated with the help of explanatory variables observed on said observation window F_(n+1) and an estimated coefficient vector also on such observation window F_(n+1); means (10) to determine an observed value of said flight parameter vector on said observation window F_(n+1); and means (7) to perform a comparison between said first and second estimations and said observed value, thereby allowing to detect a failure affecting such flight parameter vector.
 16. An automatic detection assembly for the malfunction of aircraft sensors, comprising: a device (1) such as the one specified in claim 10, to determine the development of the coefficients of the PLS regression being calculated with the help of explanatory variables and the observed parameter; and means to analyze the development of such coefficients so as to be able to detect a malfunction upon a development change for such coefficients.
 17. An aircraft system, in particular electrical flying control system, wherein it comprises a device (1) such as the one specified in claim
 13. 18. The aircraft system, in particular electrical flying control system, wherein it comprises a detection assembly (12) such as the one specified in claim
 15. 